Volunteer Summary

Table

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.43) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.04 (95% CI [18.44, 19.64], t(148) = 62.48, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.83, 95% CI [-0.06, 1.73], t(148) = 1.83, p = 0.068; Std. beta = 0.38, 95% CI [-0.03, 0.79])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.08, 0.48], t(148) = -0.76, p = 0.447; Std. beta = -0.14, 95% CI [-0.49, 0.22])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.22, 95% CI [-0.88, 1.32], t(148) = 0.39, p = 0.695; Std. beta = 0.10, 95% CI [-0.40, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.08 (95% CI [10.61, 11.55], t(148) = 46.16, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.36, 1.05], t(148) = 0.96, p = 0.338; Std. beta = 0.20, 95% CI [-0.21, 0.62])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.19, 95% CI [-0.33, 0.72], t(148) = 0.73, p = 0.467; Std. beta = 0.11, 95% CI [-0.19, 0.42])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.79, 0.69], t(148) = -0.14, p = 0.892; Std. beta = -0.03, 95% CI [-0.46, 0.40])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.38 (95% CI [43.29, 45.47], t(148) = 79.87, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 1.07, 95% CI [-0.56, 2.70], t(148) = 1.28, p = 0.199; Std. beta = 0.27, 95% CI [-0.14, 0.69])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-1.14, 0.95], t(148) = -0.18, p = 0.858; Std. beta = -0.02, 95% CI [-0.29, 0.24])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.03, 95% CI [-1.43, 1.49], t(148) = 0.04, p = 0.969; Std. beta = 7.44e-03, 95% CI [-0.37, 0.38])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.64 (95% CI [15.06, 16.22], t(148) = 52.68, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.06, 95% CI [-0.81, 0.93], t(148) = 0.13, p = 0.893; Std. beta = 0.03, 95% CI [-0.38, 0.44])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.48, 0.75], t(148) = 0.44, p = 0.663; Std. beta = 0.06, 95% CI [-0.23, 0.35])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.44, 95% CI [-0.42, 1.30], t(148) = 1.01, p = 0.313; Std. beta = 0.21, 95% CI [-0.20, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.22 (95% CI [11.82, 12.62], t(148) = 59.43, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.06, 95% CI [-0.55, 0.66], t(148) = 0.18, p = 0.858; Std. beta = 0.04, 95% CI [-0.38, 0.45])
• The effect of time point [2nd] is statistically significant and negative (beta = -0.54, 95% CI [-0.94, -0.13], t(148) = -2.57, p = 0.010; Std. beta = -0.37, 95% CI [-0.65, -0.09])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 0.73, 95% CI [0.15, 1.30], t(148) = 2.48, p = 0.013; Std. beta = 0.50, 95% CI [0.10, 0.89])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.43) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.20 (95% CI [10.80, 11.60], t(148) = 55.55, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.27, 95% CI [-0.32, 0.87], t(148) = 0.91, p = 0.363; Std. beta = 0.19, 95% CI [-0.22, 0.61])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-0.66, 0.36], t(148) = -0.58, p = 0.561; Std. beta = -0.11, 95% CI [-0.46, 0.25])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.22, 95% CI [-0.51, 0.94], t(148) = 0.59, p = 0.555; Std. beta = 0.15, 95% CI [-0.35, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.48) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.70 (95% CI [43.43, 45.97], t(148) = 69.04, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.82, 95% CI [-1.08, 2.73], t(148) = 0.85, p = 0.396; Std. beta = 0.18, 95% CI [-0.23, 0.59])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -1.55, 95% CI [-3.14, 0.03], t(148) = -1.93, p = 0.054; Std. beta = -0.34, 95% CI [-0.68, 5.86e-03])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.56, 95% CI [-1.67, 2.80], t(148) = 0.49, p = 0.623; Std. beta = 0.12, 95% CI [-0.36, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.28 (95% CI [24.27, 28.29], t(148) = 25.62, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -1.78, 95% CI [-4.80, 1.24], t(148) = -1.16, p = 0.247; Std. beta = -0.24, 95% CI [-0.65, 0.17])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 1.49, 95% CI [-0.80, 3.77], t(148) = 1.28, p = 0.202; Std. beta = 0.20, 95% CI [-0.11, 0.51])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.50, 95% CI [-4.71, 1.72], t(148) = -0.91, p = 0.362; Std. beta = -0.20, 95% CI [-0.64, 0.23])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.58 (95% CI [41.56, 47.60], t(148) = 28.93, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -2.60, 95% CI [-7.14, 1.93], t(148) = -1.13, p = 0.260; Std. beta = -0.24, 95% CI [-0.64, 0.17])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 2.98, 95% CI [-0.33, 6.29], t(148) = 1.77, p = 0.077; Std. beta = 0.27, 95% CI [-0.03, 0.57])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.93, 95% CI [-6.60, 2.73], t(148) = -0.81, p = 0.416; Std. beta = -0.17, 95% CI [-0.60, 0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 8.18e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.86 (95% CI [19.76, 21.96], t(148) = 37.22, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.84, 95% CI [-0.81, 2.49], t(148) = 1.00, p = 0.318; Std. beta = 0.22, 95% CI [-0.21, 0.64])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.04, 95% CI [-1.25, 1.18], t(148) = -0.06, p = 0.953; Std. beta = -9.47e-03, 95% CI [-0.32, 0.30])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.43, 95% CI [-2.15, 1.28], t(148) = -0.50, p = 0.620; Std. beta = -0.11, 95% CI [-0.55, 0.33])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.18 (95% CI [12.36, 14.00], t(148) = 31.50, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.49, 95% CI [-0.74, 1.73], t(148) = 0.79, p = 0.430; Std. beta = 0.17, 95% CI [-0.25, 0.59])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.27, 95% CI [-1.19, 0.65], t(148) = -0.57, p = 0.566; Std. beta = -0.09, 95% CI [-0.41, 0.22])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.46, 95% CI [-0.84, 1.76], t(148) = 0.70, p = 0.485; Std. beta = 0.16, 95% CI [-0.29, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.52) and the part related to the fixed effects alone (marginal R2) is of 1.46e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.48 (95% CI [19.85, 21.11], t(148) = 63.92, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.11, 95% CI [-1.05, 0.84], t(148) = -0.22, p = 0.827; Std. beta = -0.05, 95% CI [-0.47, 0.37])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.65, 0.85], t(148) = 0.26, p = 0.793; Std. beta = 0.04, 95% CI [-0.29, 0.38])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.93, 1.17], t(148) = 0.23, p = 0.821; Std. beta = 0.05, 95% CI [-0.41, 0.52])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.33) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.48 (95% CI [4.20, 4.76], t(148) = 31.63, p < .001). Within this model:

• The effect of group [treatment] is statistically significant and positive (beta = 0.44, 95% CI [0.03, 0.86], t(148) = 2.09, p = 0.036; Std. beta = 0.44, 95% CI [0.03, 0.84])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.30, 0.48], t(148) = 0.43, p = 0.666; Std. beta = 0.08, 95% CI [-0.30, 0.47])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.45, 0.66], t(148) = 0.36, p = 0.718; Std. beta = 0.10, 95% CI [-0.44, 0.64])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.00 (95% CI [57.36, 60.64], t(148) = 70.62, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 1.67, 95% CI [-0.78, 4.13], t(148) = 1.34, p = 0.181; Std. beta = 0.29, 95% CI [-0.13, 0.71])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-1.94, 1.74], t(148) = -0.10, p = 0.918; Std. beta = -0.02, 95% CI [-0.33, 0.30])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.25, 95% CI [-2.35, 2.84], t(148) = 0.19, p = 0.852; Std. beta = 0.04, 95% CI [-0.40, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.62 (95% CI [15.13, 16.11], t(148) = 61.87, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.84, 0.65], t(148) = -0.25, p = 0.802; Std. beta = -0.05, 95% CI [-0.47, 0.36])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.26, 95% CI [-0.85, 0.32], t(148) = -0.89, p = 0.374; Std. beta = -0.15, 95% CI [-0.47, 0.18])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.68, 95% CI [-0.14, 1.50], t(148) = 1.63, p = 0.104; Std. beta = 0.38, 95% CI [-0.08, 0.84])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 6.20e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.46 (95% CI [12.07, 12.85], t(148) = 62.66, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.29, 95% CI [-0.87, 0.30], t(148) = -0.96, p = 0.339; Std. beta = -0.20, 95% CI [-0.62, 0.21])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.08, 95% CI [-0.53, 0.38], t(148) = -0.32, p = 0.745; Std. beta = -0.05, 95% CI [-0.38, 0.27])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.37, 0.92], t(148) = 0.84, p = 0.401; Std. beta = 0.20, 95% CI [-0.26, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 6.37e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.08 (95% CI [27.26, 28.90], t(148) = 67.38, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.38, 95% CI [-1.61, 0.85], t(148) = -0.61, p = 0.543; Std. beta = -0.13, 95% CI [-0.55, 0.29])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-1.19, 0.61], t(148) = -0.63, p = 0.526; Std. beta = -0.10, 95% CI [-0.41, 0.21])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.91, 95% CI [-0.36, 2.18], t(148) = 1.40, p = 0.161; Std. beta = 0.31, 95% CI [-0.12, 0.74])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 3.97e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.06 (95% CI [13.86, 16.26], t(148) = 24.58, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.04, 95% CI [-1.84, 1.77], t(148) = -0.04, p = 0.970; Std. beta = -8.13e-03, 95% CI [-0.43, 0.41])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-1.51, 0.93], t(148) = -0.47, p = 0.639; Std. beta = -0.07, 95% CI [-0.35, 0.22])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.85, 95% CI [-0.87, 2.57], t(148) = 0.97, p = 0.331; Std. beta = 0.20, 95% CI [-0.20, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.79) and the part related to the fixed effects alone (marginal R2) is of 4.50e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.36 (95% CI [2.40, 4.32], t(148) = 6.83, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-1.36, 1.54], t(148) = 0.12, p = 0.903; Std. beta = 0.03, 95% CI [-0.39, 0.44])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.20, 95% CI [-0.58, 0.98], t(148) = 0.51, p = 0.611; Std. beta = 0.06, 95% CI [-0.17, 0.28])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.17, 95% CI [-1.27, 0.92], t(148) = -0.31, p = 0.760; Std. beta = -0.05, 95% CI [-0.36, 0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 2.32e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.96 (95% CI [2.10, 3.82], t(148) = 6.77, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-1.42, 1.15], t(148) = -0.21, p = 0.837; Std. beta = -0.04, 95% CI [-0.46, 0.37])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.30, 95% CI [-0.57, 1.17], t(148) = 0.68, p = 0.499; Std. beta = 0.10, 95% CI [-0.18, 0.38])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.15, 95% CI [-1.37, 1.07], t(148) = -0.24, p = 0.808; Std. beta = -0.05, 95% CI [-0.44, 0.34])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.74) and the part related to the fixed effects alone (marginal R2) is of 7.98e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.92 (95% CI [49.87, 53.97], t(148) = 49.65, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -1.72, 95% CI [-4.80, 1.35], t(148) = -1.10, p = 0.272; Std. beta = -0.23, 95% CI [-0.65, 0.18])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.99, 95% CI [-2.85, 0.86], t(148) = -1.05, p = 0.293; Std. beta = -0.13, 95% CI [-0.38, 0.12])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.98, 95% CI [-0.62, 4.58], t(148) = 1.49, p = 0.135; Std. beta = 0.27, 95% CI [-0.08, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 2.14e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.86 (95% CI [48.62, 53.11], t(148) = 44.40, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.62, 95% CI [-2.75, 3.99], t(148) = 0.36, p = 0.717; Std. beta = 0.08, 95% CI [-0.34, 0.50])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.33, 95% CI [-2.84, 2.19], t(148) = -0.25, p = 0.800; Std. beta = -0.04, 95% CI [-0.35, 0.27])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.25, 95% CI [-3.29, 3.79], t(148) = 0.14, p = 0.891; Std. beta = 0.03, 95% CI [-0.41, 0.47])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Table